Internet of things based intelligent accident avoidance system for adverse weather and road conditions

Abstract

Study shows that road accidents cause nearly 6,000 people to die and more than 400,000 people injured in the United States every year. Adverse weather and road conditions are some of the major reasons that contribute to 22% of accidents. People get injured and sometimes even lose their life due to road accidents that cause physical and mental instability. Regardless of the expertise of a good driver, at some point, an intelligent transportation system is necessary for the vehicle to make an immediate decision to avoid accidents. Road and weather-related mishap are those which occur due to adverse conditions like fog, winds, snow, rain, slick pavement, sleet, etc. Such accidents, though completely unavoidable, but can be reduced to some extent if proper measures are taken. Vehicle velocity, vehicle size, vehicle weight, momentum are a few of the reasons for a vehicle to go out of control. An intelligent accident avoidance system can predict the safe speed of a vehicle according to its size, weight, and momentum in different weather and road conditions. It can reduce the likelihood of accidents related to weather and road conditions. In this paper, we propose an Internet of Things (IoT) based intelligent accident avoidance system for adverse weather and road conditions. The proposed system comprises of an IoT system that perceives the environment for different weather and road conditions and a machine learning-based intelligent system that learns the adverse conditions that influence an accident to predict and suggest safe speed to the driver. The proposed system is experimented with real-time datasets and simulated using the Blynk application.